import pandas as pd
import numpy as np
import chart_studio.plotly as py
import seaborn as sns
import cufflinks as cf
import plotly.express as px
import plotly.graph_objects as go
%matplotlib inline
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
init_notebook_mode(connected = True)
cf.go_offline()

Basics

arr_1 = np.random.randn(50,4)
df_1 = pd.DataFrame(arr_1, columns= ['A','B','C','D'])
df_1.head()

	A	B	C	D
0	-0.992674	-2.236088	-1.266013	0.393762
1	0.175257	-2.090987	-1.012293	-0.333044
2	0.407436	-0.086782	0.152386	-0.360600
3	1.002256	-1.188527	-2.189232	-1.134333
4	-0.127090	-1.503851	0.103003	0.750094

df_1.plot()

<AxesSubplot:>

df_1.iplot()

Line PLots

import plotly.graph_objects as go
df_stocks = px.data.stocks()
# px.line(df_stocks, x = 'date', y = 'GOOG')
px.line(df_stocks, x ='date', y= 'GOOG', labels={'date' : 'Date', 'GOOG' : 'Price'}, title='Price value of Goole over the year')

px.line(df_stocks,x = 'date', y = ['GOOG', 'AAPL', 'AMZN', 'FB'], 
        labels = {'date': 'Date'}, title =' Apple vs Google vs Amazon vs FB')

fig = go.Figure()
fig.add_trace(go.Scatter(x= df_stocks['date'], y= df_stocks['GOOG'], name='Alphabet', mode="lines+markers"))
fig.add_trace(go.Scatter(x= df_stocks['date'], y= df_stocks['AAPL'],name='Apple', mode ="lines+markers"))
fig.add_trace(go.Scatter(x= df_stocks['date'], y= df_stocks['AMZN'],name='Amazon', mode ='lines' ))
#updates
#fig.update_layout(title = 'Stock Price Data 2018-2020', xaxis_title= 'Price', yaxis_title = 'Data')
fig.update_layout(xaxis_title= 'Price', yaxis_title = 'Data')

fig.update_layout(
xaxis=dict(showline= True, showgrid=False, showticklabels=True,linecolor= 'rgb(204,204,204)',linewidth=2,ticks='outside',
        tickfont=dict(family='Arial', size=12,color='rgb(82,82,82)')),
    
yaxis = dict(showline= False, showgrid= False, showticklabels= False, zeroline = False),

autosize =False,
margin = dict(autoexpand =False, l = 100, r =20 ,t=110) , 
showlegend = False,
plot_bgcolor ='white'
) 

Barcharts

df_uk = px.data.gapminder().query("country == 'United Kingdom'")

#df_UK = df_uk[df_uk['country'] == 'United Kingdom']  ## oneway to filter 

px.bar(df_uk, x='year', y='pop')

df_tips =px.data.tips()
px.bar(df_tips, x = 'day',y ='tip', color = 'sex', title = 'Tips by gender on each day',
      labels = {'tip': 'Total tips', 'day': 'Day of the week'})

px.bar(df_tips, x = 'sex',y ='total_bill', color = 'smoker', title = 'Tips by gender on each day',
   barmode ='group')

df_eu = px.data.gapminder().query("continent == 'Europe' and year == '2007' and pop > 2.e6 ")
fig = px.bar(df_eu, x ='country' ,y ='pop', text ='pop',color='country' )
fig.update_traces(texttemplate='%{text:.2s}', textposition='outside')
fig.update_layout(uniformtext_minsize=8)
fig.update_layout(xaxis_tickangle = -45)
fig

scatterplot

df_iris = px.data.iris()

df_iris.columns

Index(['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species',
       'species_id'],
      dtype='object')

px.scatter(df_iris, x ='sepal_width', y = 'sepal_length', color = 'species', size ='petal_length', hover_data = ['petal_width'])

fig = go.Figure(go.Scatter(
    x = df_iris['sepal_width'], y = df_iris['sepal_length'] , mode = 'markers', marker_color =  df_iris['sepal_length'],
    text = df_iris['species'], 
    marker = dict(showscale = True), marker_line_width =2, marker_size=10
))
fig.show()

fig = go.Figure(go.Scattergl(
    x = np.random.randn(100000) , y = np.random.randn(100000), mode = 'markers',
    marker = dict(color =np.random.randn(100000),
    colorscale = 'Viridis' , line_width =1 ) #, marker_line_width =2, marker_size=10
))
fig.show()

Pie Chart

df_asia = px.data.gapminder().query("year == 2007").query("continent == 'Asia'")
px.pie(df_asia, values ='pop', names = 'country', title = 'Population of asian continent', color_discrete_sequence=px.colors.sequential.RdBu)

colors = ['blue','green','black','purple','red','brown']

fig = go.Figure(data = [go.Pie(labels =['water','Grass','Normal','Psychic', 'Fire','Ground'],
                              values = [110,90,80,80,70,60])])

fig.update_traces(hoverinfo = 'label+percent', textfont_size =20, textinfo='label+percent', pull =[0.1,0.1], # pull brings out the obejct from the main pie chart
                 marker = dict(colors = colors, line=dict(color = '#FFFFFF', width =2)  ) )

fig

Histogram

dice_1 = np.random.randint(1,7,5000)
dice_2 = np.random.randint(1,7,5000)
dice_sum = dice_1 +dice_2

fig = px.histogram(dice_sum, nbins =11, 
                  title ='5000 Dice roll Hostogram',
                  marginal='violin',
                  color_discrete_sequence=['green'])
fig.update_layout(
xaxis_title_text ='Dice Roll' ,
yaxis_title_text ='Dice Sum',
bargap=0.2, showlegend= False
)


fig

df_tips = px.data.tips()
px.histogram(df_tips, x='total_bill',color='sex')

Boxplot

df_tips.columns

Index(['total_bill', 'tip', 'sex', 'smoker', 'day', 'time', 'size'], dtype='object')

df_tips = px.data.tips()
px.box(df_tips, x ='sex', y = 'tip', points ='all', color = 'sex')

fig = go.Figure(
go.Box( x = df_tips['sex'], y = df_tips['tip'],marker_color = 'blue', boxmean ='sd')
)
fig

## without using .add_trace(), if you need one box of particular field
df_stocks = px.data.stocks()
fig = go.Figure(go.Box(y = df_stocks['GOOG'] , boxpoints='all' , fillcolor= 'blue' , jitter = 0.5 ,
                       whiskerwidth= 0.2,
                       
))


fig

## using .add_trace() method

fig = go.Figure()

fig.add_trace(go.Box(y = df_stocks['GOOG'] , boxpoints='all' , fillcolor= 'blue' , jitter = 0.5 ,whiskerwidth= 0.2, name='Alphabet') )
fig.add_trace(go.Box(y = df_stocks['AAPL'] , boxpoints='all' , fillcolor= 'red' , jitter = 0.5 ,whiskerwidth= 0.2 , name='Apple'))
fig.add_trace(go.Box(y = df_stocks['FB'] , boxpoints='all' , fillcolor= 'Green' , jitter = 0.5 ,whiskerwidth= 0.2, name='Meta'))
fig.update_layout(title = "GOOG vs FB vs AAPL" , )
fig

violine plot

df_tips = px.data.tips()
px.violin(df_tips, y = 'total_bill', box = True, points ='all')

px.violin(df_tips, y = 'tip',x='smoker', color ='sex', box = True, points ='all', hover_data=df_tips.columns)

# hover_data=df_tips.columns -- > you can see the columns name while hover over the data

fig = go.Figure()
fig.add_trace(go.Violin(x=df_tips['day'][df_tips['smoker'] == 'Yes'], y = df_tips['total_bill'][df_tips['smoker'] == 'Yes'],
                       legendgroup ='Yes', scalegroup ='Yes' , name ='Yes' , line_color='blue' , side ='negative' #left side of the violineplot
                       ))

fig.add_trace(go.Violin(x=df_tips['day'][df_tips['smoker'] == 'No'], y = df_tips['total_bill'][df_tips['smoker'] == 'No'],
                       legendgroup ='Yes', scalegroup ='Yes' , name ='Yes' , line_color='red' , side ='positive' #riht side of the violineplot
                       ))

Density Heatmaps

flights = sns.load_dataset('flights')
flights.head(2)

	year	month	passengers
0	1949	Jan	112
1	1949	Feb	118

px.density_heatmap(flights, x ='year',y='month', z= 'passengers'
                  
                  )

px.density_heatmap(flights, x ='year',y='month', z= 'passengers',
                  marginal_x='histogram',
                   marginal_y='histogram'
                  )

3D Scatter Plots

flights = sns.load_dataset('flights')
fig = px.scatter_3d(flights, x ='year',y='month', z= 'passengers', color = 'year', opacity = 0.7
)
fig

3D line PLots

fig = px.line_3d(flights, x ='year',y='month',z= 'passengers', color = 'year',# opacity = 0.7 ,opacity is not allowed
)
fig

df_stocks.head()

	date	GOOG	AAPL	AMZN	FB	NFLX	MSFT
0	2018-01-01	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	2018-01-08	1.018172	1.011943	1.061881	0.959968	1.053526	1.015988
2	2018-01-15	1.032008	1.019771	1.053240	0.970243	1.049860	1.020524
3	2018-01-22	1.066783	0.980057	1.140676	1.016858	1.307681	1.066561
4	2018-01-29	1.008773	0.917143	1.163374	1.018357	1.273537	1.040708

Scatter matrix

fig= px.scatter_matrix(flights, color = 'month')
fig

Map Scatter plot

df = px.data.gapminder().query("year == 2007")
fig = px.scatter_geo(df, locations= 'iso_alpha', color = 'continent', 
                     hover_name= 'country', size ='pop', projection='orthographic')
fig

Choropleth Maps

# You can color complex maps like we do here representing unemployment data
%time
# Allows us to grab data from a supplied URL
from urllib.request import urlopen
# Used to decode JSON data
import json
# Grab US county geometry data
with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response:
    counties = json.load(response)

# Grab unemployment data based on each counties Federal Information Processing number
df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv",
                   dtype={"fips": str})

# Draw map using the county JSON data, color using unemployment values on a range of 12
fig = px.choropleth(df, geojson=counties, locations='fips', color='unemp',
                           color_continuous_scale="Viridis",
                           range_color=(0, 12),
                           scope="usa",
                           labels={'unemp':'unemployment rate'}
                          )
fig

Wall time: 0 ns

Polar Chart

df_wind = px.data.wind()
px.scatter_polar(df_wind, r="frequency", theta="direction", color="strength",
                size="frequency", symbol="strength")

# Data can also be plotted using lines radially
# A template makes the data easier to see
px.line_polar(df_wind, r="frequency", theta="direction", color="strength",
                line_close=True, template="plotly_dark", width=800, height=400)

Ternary Plot

# Used to represent ratios of 3 variables
df_exp = px.data.experiment()
px.scatter_ternary(df_exp, a="experiment_1", b="experiment_2", 
                   c='experiment_3', hover_name="group", color="gender")

Facets

df_tips = px.data.tips()
px.scatter(df_tips, x="total_bill", y="tip", color="smoker", facet_col="sex")

# We can line up data in rows and columns
px.histogram(df_tips, x="total_bill", y="tip", color="sex", facet_row="time", facet_col="day",
       category_orders={"day": ["Thur", "Fri", "Sat", "Sun"], "time": ["Lunch", "Dinner"]})

att_df

	Unnamed: 0	subject	attention	solutions	score
0	0	1	divided	1	2.0
1	1	2	divided	1	3.0
2	2	3	divided	1	3.0
3	3	4	divided	1	5.0
4	4	5	divided	1	4.0
5	5	6	divided	1	5.0
6	6	7	divided	1	5.0
7	7	8	divided	1	5.0
8	8	9	divided	1	2.0
9	9	10	divided	1	6.0
10	10	11	focused	1	6.0
11	11	12	focused	1	8.0
12	12	13	focused	1	6.0
13	13	14	focused	1	8.0
14	14	15	focused	1	8.0
15	15	16	focused	1	6.0
16	16	17	focused	1	7.0
17	17	18	focused	1	7.0
18	18	19	focused	1	5.0
19	19	20	focused	1	6.0
20	20	1	divided	2	4.0
21	21	2	divided	2	4.0
22	22	3	divided	2	5.0
23	23	4	divided	2	7.0
24	24	5	divided	2	5.0
25	25	6	divided	2	5.0
26	26	7	divided	2	4.5
27	27	8	divided	2	7.0
28	28	9	divided	2	3.0
29	29	10	divided	2	5.0
30	30	11	focused	2	5.0
31	31	12	focused	2	9.0
32	32	13	focused	2	5.0
33	33	14	focused	2	8.0
34	34	15	focused	2	8.0
35	35	16	focused	2	8.0
36	36	17	focused	2	7.0
37	37	18	focused	2	8.0
38	38	19	focused	2	6.0
39	39	20	focused	2	6.0
40	40	1	divided	3	7.0
41	41	2	divided	3	5.0
42	42	3	divided	3	6.0
43	43	4	divided	3	5.0
44	44	5	divided	3	8.0
45	45	6	divided	3	6.0
46	46	7	divided	3	6.0
47	47	8	divided	3	8.0
48	48	9	divided	3	7.0
49	49	10	divided	3	6.0
50	50	11	focused	3	6.0
51	51	12	focused	3	8.0
52	52	13	focused	3	9.0
53	53	14	focused	3	7.0
54	54	15	focused	3	7.0
55	55	16	focused	3	7.0
56	56	17	focused	3	6.0
57	57	18	focused	3	6.0
58	58	19	focused	3	6.0
59	59	20	focused	3	5.0

att_df = sns.load_dataset("attention")
fig = px.line(att_df, x='solutions', y='score', facet_col='subject',
             facet_col_wrap=5, title='Scores Based on Attention')
fig
#facet_col_wrap= --> 

Animated Plots

df_cnt = px.data.gapminder()
px.scatter(df_cnt, x ="gdpPercap", y = 'lifeExp',
          animation_frame='year',
          animation_group= 'country',
          size ='pop', color= 'continent', hover_name = 'country',
         size_max =55, range_x=[100,100000], range_y = [25,90]
          )

px.bar(df_cnt, x ="continent", y = 'pop', color='continent',
          animation_frame='year',
          animation_group= 'country', range_y =[0,4000000000] )